Regression Analysis in Medical Research
Description
Regression analysis of cause effect relationships is increasingly the core of medical and health research. This work is a 2nd edition of a 2017 pretty complete textbook and tutorial for students as well as recollection / update bench and help desk for professionals.
It came to the authors' attention, that information of history, background, and purposes, of the regression methods addressed were scanty. Lacking information about all of that has now been entirely covered.
The editorial art work of the first edition, however pretty, was less appreciated by some readerships, than were the original output sheets from the statistical programs as used. Therefore, the editorial art work has now been systematically replaced with original statistical software tables and graphs for the benefit of an improved usage and understanding of the methods.
In the past few years, professionals have been flooded with big data. The Covid-19 pandemic gave cause for statistical software companies to foster novel analytic programs better accounting outliers and skewness. Novel fields of regression analysis adequate for such data, like sparse canonical regressions and quantile regressions, have been included.
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Persons
Professor Zwinderman is one of the Principle Investigators of the Academic Medical Center Amsterdam, and his research is concerned with developing statistical methods for new research designs in biomedical science, particularly integrating omics data, like genomics, proteomics, metabolomics, and analysis tools based on parallel computing and the use of cluster computers and grid computing.
Professor Cleophas is a member of the Academic Committee of the European College of Pharmaceutical Medicine, that provides, on behalf of 22 European Universities, the Master-ship trainings "Pharmaceutical Medicine" and "Medicines Development".
From their expertisethey should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 18 years, and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
The authors as professors and teachers in statistics at universities in The Netherlands and France for the most part of their lives, are concerned, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis, and they do hope that the current production "Regression Analysis for Starters and 2nd Levelers" will be a helpful companion for the purpose. Five textbookscomplementary to the current production and written by the same authors are
Statistics applied to clinical studies 5th edition, 2012, Machine learning in medicine a complete overview, 2015, SPSS for starters and 2nd levelers 2nd edition, 2015, Clinical data analysis on a pocket calculator 2nd edition, 2016, Modern Meta-analysis, 2017, Regression Analysis in Medical Research, 2018,
Modern Bayesian Statistics in Clinical Research, 2018, Analysis of Safety Data of Drug Trials, 2019, Efficacy Analysis in Clinical Trials, 2019
All published by Springer
Content
Preface.- Continuous Outcome Regressions.- Dichotomous Outcome Regressions.- Confirmative Regressions.- Dichotomous Regressions Other than Logistic and Cox.- Polytomous Outcome Regressions.- Time to Event Regressions other than Traditional Cox.- Analysis of Variance (ANOVA).- Repeated Outcomes Regression Methods.- Methodologies for Better Fit of Categorical Predictors.- Laplace Regressions, Multi- instead of Mono-Exponential Models.- Regressions For Making Extrapolations..- Standardized Regression Coefficients.- Multivariate Analysis of Variance and Canonical Regression.- More on Poisson Regressions.- Regression Trend Testing.- Optimal Scaling and Automatic Linear Regression.- Spline Regressions.- More on Nonlinear Regressions.- Special Forms of Continuous Outcome Regressions.- Regressions for Quantitative Diagnostic Testing.- Regressions, a Panacee or at Least a Widespread Help for Data Analyses.- Regression Trees.- Regressions with Latent Variables.- Partial Correlations.- Functional Data Analysis Basis.- Functional Data Analysis Advanced.- Quantile Regression.- Index.
